Instance normalization proved quite influential. Normalizing the representation extracted from the content allowed training networks which could produce outputs in a multiple predefined styles (typically 30-50). Chen et al. [1]} proposed training an architecture where the parameters of most layers were shared, but a few were learned separately for each style, and were swapped in and out depending on the target style. Dumoulin et al. [2]} had a similar, but simpler approach, learning a per-style scaling and bias to apply after instance normalization.
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